Collaborative robot true trajectories and pose estimations

This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".Abstract of the paper: Anomalous robot motions caused by cyber attacks and inherent defects can lead to task failures as well as harmful accidents in collaborative human-robot workplaces. External sensors are essential for reliably monitoring robot tool paths and detecting any deviation as early as possible, especially in anomalous situations where the robot system and its internal sensors may be out-of-control. However, affordable external sensors usually suffer from poor-quality measurements, necessitating enhancements. In addition to external sensors providing independent monitoring, to effectively detect trajectory deviations, we need anomaly detection methods that can capture complex dynamic patterns in the nonlinear trajectory time-series data. In this work, we propose a framework to accurately estimate robot trajectories during normal robot operations as well as to detect various types of anomalous trajectory changes using an external camera. The proposed framework efficiently incorporates marker-based pose estimation, Long Short-Term Memory (LSTM), and residual control charts. The framework was evaluated in a shared human-robot assembly task. The results show that we can accurately estimate robot trajectories by enhancing camera-based measurements. Moreover, it effectively detects anomalous trajectory changes in their early stages. The motion deviation upon detection assists in determining a safe working distance. The framework also exhibits generalizability to previously unseen trajectory deviations and possesses adaptability to other types of external sensors.

本数据集系我方论文《在协作机器人制造系统中检测异常机器人运动》所使用的部分数据集。论文摘要如下：由网络攻击和固有缺陷引起的异常机器人运动可能导致协作人机工作场所中的任务失败及有害事故。外部传感器对于可靠地监控机器人工具路径以及在异常情况下尽早检测任何偏差至关重要，尤其是在机器人系统和其内部传感器可能失控的异常情况下。然而，经济实惠的外部传感器通常受限于测量质量欠佳，因而需要提升。除了外部传感器提供独立监控之外，为了有效检测轨迹偏差，我们还需要能够捕捉非线性轨迹时间序列数据中复杂动态模式的异常检测方法。在本研究中，我们提出了一种框架，用于在正常机器人操作期间精确估计机器人轨迹，并利用外部摄像头检测各种类型的异常轨迹变化。所提出的框架高效地集成了基于标记的姿态估计、长短期记忆（LSTM）和残差控制图。该框架在共享人机装配任务中进行了评估。结果显示，通过增强基于摄像头的测量，我们可以精确估计机器人轨迹。此外，该框架能够有效地在早期阶段检测到异常轨迹变化。检测到的运动偏差有助于确定安全的工作距离。该框架还表现出对先前未见轨迹偏差的泛化能力，并具备适应其他类型外部传感器的灵活性。